[CLIP] Update clip dependencies, README.md, export pathways (#1711)

* fix instructions; export pathways

* update variable; quality

integrations/clip/README.md

@@ -16,11 +16,21 @@ limitations under the License.
 
 # CLIP Export Examples
 
-The examples in `clip_onnx_export.py` provide the steps needed to export a CLIP model using sparseml's onnx exporting functionality. The models and pretrained weights are pulled in from [OpenClip](https://github.com/mlfoundations/open_clip/tree/main) and the command line tools provided allow exporting of a given model's Text and Visual branches. See the OpenClip repository for a full list of available models. For the CoCa models available in OpenClip, an additional text-decoder is also exported.
+The examples in `clip_onnx_export.py` provide the steps needed to export a CLIP model using sparseml's onnx exporting functionality. The models and pretrained weights are pulled in from [OpenClip](https://github.com/mlfoundations/open_clip/tree/main) and the command line tools provided allow exporting of a given model's Text and Visual branches. See the OpenClip repository for a full list of available models. For the CoCa/Caption models available in OpenClip, an additional text-decoder is also exported.
 
 ## Installation
 
-The examples provided require torch nighly and `open_clip_torch==2.20.0` to be installed. To work within the `sparseml` environment, be sure to set the environment variable `MAX_TORCH` to your installed version when 
+The examples provided require `open_clip_torch==2.20.0` to be installed along with **torch nighly**. To work within the `sparseml` environment, be sure to set the environment variable `MAX_TORCH` to your installed version when 
 installing torch nightly.
 
-Example: `MAX_TORCH="2.1.0.dev20230613+cpu"`
+Steps:
+- Install `sparseml[clip]`. This will ensure open_clip_torch is installed
+- Uninstall torch by running:  
+```
+   pip uninstall torch
+```
+- Install torch nightly:  
+```   
+   pip install --pre torch --index-url https://download.pytorch.org/whl/nightly/
+```
+- Set your environment variable to the correct torch version: Example: `export MAX_TORCH="2.1.0.dev20230613+cpu"`

integrations/clip/clip_models.py

@@ -16,18 +16,6 @@
 import torch.nn as nn
 
 
-class VisualModel(nn.Module):
-    def __init__(self, visual_model: torch.nn.Module, output_tokens: bool):
-
-        super().__init__()
-
-        self.visual_model = visual_model
-        self.visual_model.output_tokens = output_tokens
-
-    def forward(self, x):
-        return self.visual_model(x)
-
-
 class TextModel(nn.Module):
     def __init__(
         self,

integrations/clip/clip_onnx_export.py

@@ -28,7 +28,7 @@
 import torch
 
 import open_clip
-from clip_models import TextModel, VisualModel
+from clip_models import TextModel
 from sparseml.pytorch.utils import export_onnx
 
 
@@ -61,12 +61,9 @@ def _export_visual(
     **export_kwargs,
 ):
     module_name = "clip_visual.onnx"
-    visual_model = VisualModel(
-        visual_model=model.visual,
-        output_tokens=is_coca,
-    )
+    visual_model = model.visual
 
-    image_shape = visual_model.visual_model.image_size[0]
+    image_shape = visual_model.image_size[0]
     sample_input = torch.randn(1, 3, image_shape, image_shape, requires_grad=True)
 
     visual_model = visual_model.to(device)
@@ -107,10 +104,11 @@ def _export_text(
 
     text_model = text_model.to(device)
     text_model.eval()
-    sample_batch = tokenizer(["a dog"])
 
     if is_coca:
-        sample_batch = sample_batch[:, :-1]
+        sample_batch = torch.ones(6, 15, dtype=torch.long)
+    else:
+        sample_batch = tokenizer(["a dog"]).to(torch.int32)
 
     _export_onnx(
         module=text_model,
@@ -130,9 +128,7 @@ def _export_text_decoder(
 
     sample_batch = OrderedDict()
     sample_batch["image_embs"] = torch.randn(1, 255, model.text.output_dim)
-    sample_batch["text_embs"] = torch.randn(
-        1, model.text.context_length, model.text.output_dim
-    )
+    sample_batch["text_embs"] = torch.randn(1, 15, model.text.output_dim)
 
     _export_onnx(
         module=decoder,
@@ -175,13 +171,13 @@ def main():
     parser.add_argument(
         "--model",
         type=str,
-        default="ViT-B-32",
+        default="coca_ViT-B-32",
         help="Name of CLIP model. See OpenClip docs for a list of available models",
     )
     parser.add_argument(
         "--pretrained",
         type=str,
-        default="laion2b_s34b_b79k",
+        default="mscoco_finetuned_laion2b_s13b_b90k",
         help="Name of the pretraining to use. See OpenClip docs for a list of options.",
     )
     parser.add_argument(